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DriftGuard

DriftGuard is a semantic mistake-memory and guardrail layer for autonomous agents.

It sits between intent and execution, allowing agents to learn from past failures and avoid repeating them.

The problem​

Agents today can act. They usually cannot remember mistakes meaningfully.

agent makes mistake
agent retries
agent repeats mistake
agent retries again

The solution​

DriftGuard introduces a semantic failure memory layer:

plan step
↓
DriftGuard review
↓
warning surfaced
↓
agent revises action

What DriftGuard stores​

Every recorded mistake becomes a causal chain in a semantic graph:

action → feedback → outcome

For example:

"increase salt" → "too salty" → "dish ruined"

When a similar action appears later — like "add more salt" or "season aggressively" — DriftGuard retrieves the warning before execution.

What DriftGuard provides​

  • Semantic mistake memory
  • Semantic success memory with positive reinforcement
  • Similarity-aware warning and reinforcement retrieval
  • Policy-based execution guardrails
  • Merge and deduplicate memory graphs
  • JSON, SQLite, or Postgres persistence
  • Runtime metrics and observability
  • Pruning of stale weak memories
  • MCP server integration
  • LangGraph and generic adapters
  • Offline benchmark harness

When to use DriftGuard​

DriftGuard helps when your agent:

  • Retries failing steps repeatedly
  • Forgets past execution errors
  • Needs execution-time guardrails
  • Requires semantic mistake recall
  • Runs multi-step planners
  • Uses LangGraph or MCP
  • Executes tools autonomously

Project status​

Current release includes the full semantic merge engine, retrieval engine, graph persistence, MCP server, LangGraph adapter, benchmark harness, runtime metrics, pruning engine, and pytest coverage.

DriftGuard is suitable for early production experimentation and agent-infrastructure research workflows.